# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.

# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
# --------------------------------------------------------
# References:
# DeiT: https://github.com/facebookresearch/deit
# BEiT: https://github.com/microsoft/unilm/tree/master/beit
# --------------------------------------------------------

import builtins
import datetime
import os
import subprocess
import time
from collections import defaultdict, deque
from pathlib import Path

import torch
import torch.distributed as dist
from torch import inf


class SmoothedValue(object):
    """Track a series of values and provide access to smoothed values over a
    window or the global series average.
    """

    def __init__(self, window_size=20, fmt=None):
        if fmt is None:
            fmt = "{median:.4f} ({global_avg:.4f})"
        self.deque = deque(maxlen=window_size)
        self.total = 0.0
        self.count = 0
        self.fmt = fmt

    def update(self, value, n=1):
        self.deque.append(value)
        self.count += n
        self.total += value * n

    def synchronize_between_processes(self):
        """
        Warning: does not synchronize the deque!
        """
        if not is_dist_avail_and_initialized():
            return
        t = torch.tensor([self.count, self.total], dtype=torch.float64, device="cuda")
        dist.barrier()
        dist.all_reduce(t)
        t = t.tolist()
        self.count = int(t[0])
        self.total = t[1]

    @property
    def median(self):
        d = torch.tensor(list(self.deque))
        return d.median().item()

    @property
    def avg(self):
        d = torch.tensor(list(self.deque), dtype=torch.float32)
        return d.mean().item()

    @property
    def global_avg(self):
        return self.total / self.count

    @property
    def max(self):
        return max(self.deque)

    @property
    def value(self):
        return self.deque[-1]

    def __str__(self):
        return self.fmt.format(
            median=self.median, avg=self.avg, global_avg=self.global_avg, max=self.max, value=self.value
        )


class MetricLogger(object):
    def __init__(self, delimiter="\t"):
        self.meters = defaultdict(SmoothedValue)
        self.delimiter = delimiter

    def update(self, **kwargs):
        for k, v in kwargs.items():
            if v is None:
                continue
            if isinstance(v, torch.Tensor):
                v = v.item()
            assert isinstance(v, (float, int))
            self.meters[k].update(v)

    def __getattr__(self, attr):
        if attr in self.meters:
            return self.meters[attr]
        if attr in self.__dict__:
            return self.__dict__[attr]
        raise AttributeError("'{}' object has no attribute '{}'".format(type(self).__name__, attr))

    def __str__(self):
        loss_str = []
        for name, meter in self.meters.items():
            loss_str.append("{}: {}".format(name, str(meter)))
        return self.delimiter.join(loss_str)

    def synchronize_between_processes(self):
        for meter in self.meters.values():
            meter.synchronize_between_processes()

    def add_meter(self, name, meter):
        self.meters[name] = meter

    def log_every(self, iterable, print_freq, header=None):
        i = 0
        if not header:
            header = ""
        start_time = time.time()
        end = time.time()
        iter_time = SmoothedValue(fmt="{avg:.4f}")
        data_time = SmoothedValue(fmt="{avg:.4f}")
        space_fmt = ":" + str(len(str(len(iterable)))) + "d"
        log_msg = [header, "[{0" + space_fmt + "}/{1}]", "eta: {eta}", "{meters}", "time: {time}", "data: {data}"]
        if torch.cuda.is_available():
            log_msg.append("max mem: {memory:.0f}")
        log_msg = self.delimiter.join(log_msg)
        MB = 1024.0 * 1024.0
        for obj in iterable:
            data_time.update(time.time() - end)
            yield obj
            iter_time.update(time.time() - end)
            if i % print_freq == 0 or i == len(iterable) - 1:
                eta_seconds = iter_time.global_avg * (len(iterable) - i)
                eta_string = str(datetime.timedelta(seconds=int(eta_seconds)))
                if torch.cuda.is_available():
                    print(
                        log_msg.format(
                            i,
                            len(iterable),
                            eta=eta_string,
                            meters=str(self),
                            time=str(iter_time),
                            data=str(data_time),
                            memory=torch.cuda.max_memory_allocated() / MB,
                        )
                    )
                else:
                    print(
                        log_msg.format(
                            i, len(iterable), eta=eta_string, meters=str(self), time=str(iter_time), data=str(data_time)
                        )
                    )
            i += 1
            end = time.time()
        total_time = time.time() - start_time
        total_time_str = str(datetime.timedelta(seconds=int(total_time)))
        print("{} Total time: {} ({:.4f} s / it)".format(header, total_time_str, total_time / len(iterable)))


def setup_for_distributed(is_master):
    """
    This function disables printing when not in master process
    """
    builtin_print = builtins.print

    def print(*args, **kwargs):
        force = kwargs.pop("force", False)
        if is_master or force:
            now = datetime.datetime.now().time()
            builtin_print("[{}] ".format(now), end="")  # print with time stamp
            builtin_print(*args, **kwargs)

    builtins.print = print


def is_dist_avail_and_initialized():
    if not dist.is_available():
        return False
    if not dist.is_initialized():
        return False
    return True


def get_world_size():
    if not is_dist_avail_and_initialized():
        return 1
    return dist.get_world_size()


def get_rank():
    if not is_dist_avail_and_initialized():
        return 0
    return dist.get_rank()


def is_main_process():
    return get_rank() == 0


def save_on_master(*args, **kwargs):
    if is_main_process():
        torch.save(*args, **kwargs)


def init_distributed_mode():
    if "RANK" in os.environ and "WORLD_SIZE" in os.environ:
        # torchrun
        rank = int(os.environ["RANK"])
        world_size = int(os.environ["WORLD_SIZE"])
        gpu = int(os.environ["LOCAL_RANK"])
    elif "SLURM_PROCID" in os.environ:
        # slurm
        rank = int(os.environ["SLURM_PROCID"])
        world_size = int(os.environ["SLURM_NPROCS"])
        if "MASTER_PORT" not in os.environ:
            os.environ["MASTER_PORT"] = "8964"
        os.environ["MASTER_ADDR"] = (
            subprocess.check_output('scontrol show hostnames "$SLURM_JOB_NODELIST"', shell=True)
            .decode("utf-8")
            .splitlines()[0]
        )
        gpu = rank % torch.cuda.device_count()
    else:
        print(
            "Only distributed mode is supported but distributed env cannot be initialized. "
            "Use torchrun --nproc_per_node 1 ... to run on a single gpu."
        )

    torch.cuda.set_device(gpu)
    print("| distributed init (rank {}), gpu {}".format(rank, gpu), flush=True)
    torch.distributed.init_process_group("nccl", world_size=world_size, rank=rank)
    torch.distributed.barrier()
    setup_for_distributed(rank == 0)


class NativeScalerWithGradNormCount:
    state_dict_key = "amp_scaler"

    def __init__(self):
        self._scaler = torch.cuda.amp.GradScaler()

    def __call__(self, loss, optimizer, clip_grad=None, parameters=None, create_graph=False, update_grad=True):
        self._scaler.scale(loss).backward(create_graph=create_graph)
        if update_grad:
            if clip_grad is not None:
                assert parameters is not None
                self._scaler.unscale_(optimizer)  # unscale the gradients of optimizer's assigned params in-place
                norm = torch.nn.utils.clip_grad_norm_(parameters, clip_grad)
            else:
                self._scaler.unscale_(optimizer)
                norm = get_grad_norm_(parameters)
            self._scaler.step(optimizer)
            self._scaler.update()
        else:
            norm = None
        return norm

    def state_dict(self):
        return self._scaler.state_dict()

    def load_state_dict(self, state_dict):
        self._scaler.load_state_dict(state_dict)


def get_grad_norm_(parameters, norm_type: float = 2.0) -> torch.Tensor:
    if isinstance(parameters, torch.Tensor):
        parameters = [parameters]
    parameters = [p for p in parameters if p.grad is not None]
    norm_type = float(norm_type)
    if len(parameters) == 0:
        return torch.tensor(0.0)
    device = parameters[0].grad.device
    if norm_type == inf:
        total_norm = max(p.grad.detach().abs().max().to(device) for p in parameters)
    else:
        total_norm = torch.norm(
            torch.stack([torch.norm(p.grad.detach(), norm_type).to(device) for p in parameters]), norm_type
        )
    return total_norm


def save_model(args, epoch, model, model_without_ddp, optimizer, loss_scaler):
    output_dir = Path(args.output_dir)
    epoch_name = str(epoch)
    if loss_scaler is not None:
        checkpoint_paths = [output_dir / ("checkpoint-%s.pth" % epoch_name)]
        model_state_dict = model_without_ddp.state_dict()
        for n, p in model_without_ddp.named_parameters():
            if not p.requires_grad:
                del model_state_dict[n]
        for checkpoint_path in checkpoint_paths:
            to_save = {
                "model": model_state_dict,
                "optimizer": optimizer.state_dict(),
                "epoch": epoch,
                "scaler": loss_scaler.state_dict(),
                "args": args,
            }

            save_on_master(to_save, checkpoint_path)
    else:
        client_state = {"epoch": epoch}
        model.save_checkpoint(save_dir=args.output_dir, tag="checkpoint-%s" % epoch_name, client_state=client_state)


def load_model(args, model_without_ddp, optimizer, loss_scaler):
    if args.auto_resume and not args.resume:
        print("Trying to auto resume...")
        output_dir = Path(args.output_dir)
        ckpts = output_dir.glob("*.pth")
        ckpt_iters = [int(x.stem[len("checkpoint-") :]) for x in ckpts]
        ckpt_iters.sort()
        print("Candidate checkpoints:", [str(Path(args.output_dir) / ("checkpoint-%d.pth" % x)) for x in ckpt_iters])
        for x in ckpt_iters[::-1]:
            ckpt_path = str(Path(args.output_dir) / ("checkpoint-%d.pth" % x))
            try:
                torch.load(ckpt_path, map_location="cpu")
            except Exception as e:
                print("Failed to load %s with error:" % ckpt_path, e)
                continue
            print("Found a valid checkpoint:", ckpt_path)
            args.resume = ckpt_path
            break

    if args.resume:
        if args.resume.startswith("https"):
            checkpoint = torch.hub.load_state_dict_from_url(args.resume, map_location="cpu", check_hash=True)
        else:
            checkpoint = torch.load(args.resume, map_location="cpu")
        missing_keys, unexpected_keys = model_without_ddp.load_state_dict(checkpoint["model"], strict=False)
        assert len(unexpected_keys) == 0
        missing_keys = set(missing_keys)
        for n, p in model_without_ddp.named_parameters():
            if p.requires_grad:
                assert n not in missing_keys
        print("Resume checkpoint %s" % args.resume)
        if "optimizer" in checkpoint and "epoch" in checkpoint and not (hasattr(args, "eval") and args.eval):
            optimizer.load_state_dict(checkpoint["optimizer"])
            args.start_epoch = checkpoint["epoch"] + 1
            if "scaler" in checkpoint:
                loss_scaler.load_state_dict(checkpoint["scaler"])
            print("With optim & sched!")


def all_reduce_mean(x):
    world_size = get_world_size()
    if world_size > 1:
        x_reduce = torch.tensor(x).cuda()
        dist.all_reduce(x_reduce)
        x_reduce /= world_size
        return x_reduce.item()
    else:
        return x
